and EfficientNet are highly accurate for image classification. There are also deep learning networks that are trained on large image datasets, such as ImageNet, which has over 11 million images and almost 11,000 classes. After a network is trained with ImageNet data, you can then fine-tune ...
However, there is a lot of work that comes with solving object recognition problems through deep learning as it requires sufficient graphical processing unit (GPU) power and a large training dataset. The CNN is a deep learning model that solves complex computer vision tasks through artificial ...
How Well Do Sparse ImageNet Models Transfer? Eugenia Iofinova* IST Austria Alexandra Peste* IST Austria Mark Kurtz Neural Magic Dan Alistarh IST Austria & Neural Magic Abstract Transfer learning is a classic paradigm by which mod- els pretrained on large "upstream" datasets are adapted to yiel...
Computational biologists have tried to replicate this approach by constructing training datasets that were either diverse (Cellpose) or large (TissueNet, LiveCell). Yet even models trained on these datasets can fail on new categories of images (for example, the Cellpose model on TissueNet or Live...
from sagemaker import image_uris, model_uris, script_uris, hyperparameters from sagemaker.estimator import Estimator model_id, model_version = "tensorflow-ic-imagenet-mobilenet-v2-100-224-classification-4", "*" training_instance_type = "ml.p3.2xlarge" # Retrieve the Docker image train_image_...
(2009). ImageNet: A large-scale hierarchical image database. In CVPR. Dou, Q., de Castro, D. C., Kamnitsas, K., & Glocker, B. (2019). Domain generalization via model-agnostic learning of semantic features. In Advances in Neural Information Processing Systems (pp. 6447–6458). Dz...
we evaluate whether recently proposed watermarking schemes that claim robustness are robust against a large set of removal attacks. We survey methods from the literature that (i) are known removal attacks, (ii) derive surrogate models but have not been evaluated as removal attacks, and (iii) nov...
Large-scale machine learning with stochastic gradient descent. In Proc. COMPSTAT'2010 (eds Aguilera, A. M. et al.) 177–186 (Physica-Verlag HD, 2010). Zhou, D. et al. EcoNAS: finding proxies for economical neural architecture search. In Proc. IEEE Conference on Computer Vision and ...
(GNN) have succeeded in exploring the underlying topological relationship among the graph data with a few graph neural layers. Unfortunately, it cannot be directly utilized on non-graph data due to the lack of graph structure and has high inference latency on large-scale scenarios. Inspired by ...
Interest in the technique exploded after 2010, following the introduction of ImageNet -- a large, labeled database of images -- and the launch of its annual ImageNet Large Scale Visual Recognition Challenge (ILSVRC). One of the most promising entries in the inaugural year of the competition ...